Managing dependent delete operations among data stores

ABSTRACT

Example distributed storage systems, delete managers, and methods provide for managing dependent delete operations among data stores. Dependent data operation entries and corresponding dependency sets may be identified in an operations log. Dependent data operations may be identified in each shard and data operation entries. A delete process for the data objects in the dependency set may be delayed until the delete process for the dependent data object completes.

TECHNICAL FIELD

The present disclosure generally relates to data storage, and in a more particular example, to transferring dependent data between data stores.

BACKGROUND

Often, distributed storage systems are used to store large amounts (e.g., terabytes, petabytes, exabytes, etc.) of data, such as objects or files in a distributed and fault tolerant manner with a predetermined level of redundancy.

Some existing object storage systems store data objects referenced by an object identifier versus file systems. This can generally allow object storage systems to surpass the maximum limits for storage capacity of file systems in a flexible way such that, for example, storage capacity can be added or removed as a function of the applications, systems, and/or enterprise needs, while reducing degradation in performance as the system grows. As a result, object storage systems are often selected for large-scale storage systems.

A storage system node may store a set of data objects and/or object versions and those objects may be replicated to one or more other storage system nodes to provide redundancy for data recovery, response time, volume, load balancing, data retention policies, and other considerations. In some configurations, all objects from a node are copied to one or more other nodes and vice versa, in an attempt to synchronize the data objects present on each node. Replication may be unidirectional or multidirectional, and scalable systems may employ parallel replication to improve efficiency, reliability, and throughput of such systems. Service and network variations, system failures, and parallel replication may mean that replication order cannot be assumed or guaranteed based solely on the order of a replication queue or log from which the replication requests are being generated. Each data object may move between nodes independent of the other data objects.

In some configurations, some data objects may be dependent upon other data objects to be valid. For example, some systems use snapshots of higher-level structures, such as all data objects in a node or logical structure, data object metadata, log information, databases, data structures pointing to a blobstore, or similar aggregations of information related to other data objects, and these snapshots may cease to be valid if the underlying set of data objects (and their related metadata) change. This may create problems when data objects are replicated out of order and a system can no longer guarantee that the dependent data object has all of the dependencies intact. For example, when a marker or dependent data object that references a set of other objects arrives at a node while the data objects to which it refers are delayed or fail to replicate due to a system crash, network error, or other problem, the resulting marker object, snapshot, or other dependent data object may be invalid.

Some systems have used strictly ordered replication with confirmation, such as an ordered replication journal, to avoid problems created by out-of-order replication. However, such systems may not scale well when nodes include a billion or more objects and scans to identify missing data objects may require more time and computational resources than are practical. Forcing one-at-a-time replication may be prohibitive to replicating large systems in a timely and resource efficient manner. Similarly, auditing the set of dependency objects after the dependent data object is received, in order to detect missing objects after replication, may be impractical for large data sets.

As large-scale storage systems scale and create more dependent data objects that depend on increasingly large dependency sets, reliable and efficient implementations for managing deletion of dependent data objects may be needed. A need exists for at least management of dependent delete operations among data stores.

SUMMARY

Various aspects for managing delete operations for dependent data objects in data stores, particularly, using a series of delays to manage dependent delete operations among data stores, including sharded data stores, are described.

One general aspect includes a system that includes a first operation log associated with a first storage node, a barrier engine, and a delete manager. The barrier engine is configured to: determine a first dependent operation entry in the first operations log indicating a first dependent data object at a first timestamp; and determine at least one data operation entry in the first operations log for a first dependency set of data objects, where a validity of the first dependent data object depends on the first dependency set of data objects. The delete manager is configured to: determine a first dependent delete request for the first dependent data object; determine at least one dependency delete request for at least one data object in the first dependency set of data objects; execute a delete process the first dependent delete request for the first dependent data object; and delay, until the delete process for the first dependent delete request completes, delete processing of the at least one dependency delete request.

Implementations may include one or more of the following features. The barrier engine may be further configured to monitor a time interval from a first time to a second time, where the first timestamp is no earlier than the first time and prior to the second time. The delete manager may be further configured to delay delete processing of the at least one dependency delete request until after the second time. The system may include a second storage node and a replication manager configured to replicate data objects between the first storage node and the second storage node by: replicating the first dependent delete request prior to the second time; and replicating, between the first time and the second time, dependency delete requests with timestamps prior to the first time. The replication manager may be further configured to replicate data objects between the first storage node and second storage node by replicating, after the second time, the at least one dependency delete request for the first dependent data object. The barrier engine may be further configured to determine a second dependent operation entry in the first operations log indicating a second dependent data object at a second timestamp, where the first dependent data object is a snapshot of the first storage node at the first time and the second dependent data object is a snapshot of the first storage node at the second time. The barrier engine may be further configured to: generate the first dependent data object at the first time; determine the time interval between the first time and the second time using a predetermined snapshot interval; and generate the second dependent data object at the second time. The barrier engine may be further configured to: increase, until replication of the first dependent delete request to a second storage node, the time interval; and delay, responsive to increasing the time interval, generation of the second dependent data object. The delete manager may be further configured to: generate, at a first time of the first timestamp, a first operation queue of dependent delete requests, including the first dependent delete request; and generate, at the first time, a second operation queue of delete requests, excluding the first dependent delete request and including the at least one dependency delete request for the at least one data object in the first dependency set of data objects. The system may include a second operations log associated with a second storage node and a replication manager configured to replicate data objects between the first storage node and the second storage node. The barrier engine may be further configured to determine the first dependent operation entry in the second operation log indicating the first dependent data object. The delete manager may be further configured to: execute a delete process the first dependent delete request for the first dependent data object in both the first operations log and the second operations log; and delay, until the delete process for the first dependent delete request completes in the first operation log and the second operation log, delete processing of the at least one dependency delete request.

Another general aspect includes a computer-implemented method that includes: storing a first operations log associated with a first storage node; determining a first dependent operation entry in the first operations log indicating a first dependent data object at a first timestamp; determining at least one data operation entry in the first operations log for a first dependency set of data objects, where a validity of the first dependent data object depends on the first dependency set of data objects; determining a first dependent delete request for the first dependent data object; determining at least one dependency delete request for at least one data object in the first dependency set of data objects; executing a delete process for the first dependent delete request for the first dependent data object; and delaying, until the delete process for the first dependent delete request completes, the delete process for the at least one dependency delete request.

Implementations may include one or more of the following features. The computer-implemented method may include: monitoring a time interval from a first time to a second time, where: the first timestamp is no earlier than the first time and prior to the second time; and delaying the delete process of the at least one dependency delete request includes waiting until after the second time. The computer-implemented method may include replicating data objects between the first storage node and a second storage node by: replicating, prior to the second time, the first dependent delete request; and replicating, prior to the second time, dependency delete requests with timestamps prior to the first time. Replicating data objects between the first storage node and second storage node may further include replicating, after the second time, the at least one dependency delete request for the first dependent data object. The computer-implemented method may include determining a second dependent operation entry in the first operations log indicating a second dependent data object at a second timestamp, where the first dependent data object is a snapshot of the first storage node at the first time and the second dependent data object is a snapshot of the first storage node at the second time. The computer-implemented method may include: generating the first dependent data object at the first time; determining the time interval between the first time and the second time using a predetermined snapshot interval; and generating the second dependent data object at the second time. The computer-implemented method may include: increasing, until replication of the first dependent delete request to a second storage node, the time interval; and delaying, responsive to increasing the time interval, generation of the second dependent data object. The computer-implemented method may include: generating a first operation queue of dependent delete requests, including the first dependent delete request; and generating a second operation queue of delete requests, excluding the first dependent delete request and including the at least one dependency delete request for the at least one data object in the first dependency set of data objects. The computer-implemented method may include: executing the delete process for the first operation queue no later than the second time; and executing the delete process for the second operation queue after the second time. The computer-implemented method may include: storing a second operations log associated with a second storage node; replicating data objects between the first storage node and the second storage node; determining the first dependent operation entry in the second operations log indicating the first dependent data object; executing a delete process for the first dependent delete request for the first dependent data object in both the first operations log and the second operations log; and delaying, until the delete process for the first dependent delete request completes in both the first operations log and the second operation log, the delete process of the at least one dependency delete request in both the first operations log and the second operations log.

Another general aspect includes a system that includes: a first operations log associated with a first storage node; means for determining a first dependent operation entry in the first operations log indicating a first dependent data object at a first timestamp; means for determining at least one data operation entry in the first operations log for a first dependency set of data objects, where a validity of the first dependent data object depends on the first dependency set of data objects; means for determining a first dependent delete request for the first dependent data object; means for determining at least one dependency delete request for at least one data object in the first dependency set of data objects; means for delete processing the first dependent delete request for the first dependent data object; and means for delaying, until the delete processing for the first dependent delete request completes, delete processing of the at least one dependency delete request.

The various embodiments advantageously apply the teachings of distributed storage networks and/or systems to improve the functionality of such computer systems. The various embodiments include operations to overcome or at least reduce the issues in the previous storage networks and/or systems discussed above and, accordingly, are more reliable and/or efficient than other computing networks. That is, the various embodiments disclosed herein include hardware and/or software with functionality to improve dependent delete operations among data stores, such as by using timestamps and delays in time-based operations logs to enforce limited ordered deletions. Accordingly, the embodiments disclosed herein provide various improvements to storage networks and/or storage systems.

It should be understood that language used in the present disclosure has been principally selected for readability and instructional purposes, and not to limit the scope of the subject matter disclosed herein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 schematically illustrates an example of a distributed storage system.

FIG. 2 schematically illustrates an example client architecture in which the distributed storage system of FIG. 1 may operate.

FIG. 3 schematically illustrates an example of a storage node of the distributed storage system of FIG. 1 .

FIG. 4 schematically illustrates an example of a controller node or access node of the distributed storage system of FIG. 1 .

FIG. 5 schematically illustrates some example elements of an object storage system for the distributed storage system of FIG. 1 .

FIG. 6 schematically illustrates data objects transferred between example data stores using replication barriers.

FIG. 7 schematically illustrates data objects transferred in a sharded object storage system.

FIGS. 8A-8D schematically illustrate dependent data operations managed across shards at a series of time points.

FIG. 9 schematically illustrates time-based management of dependent delete operations and replication.

FIG. 10 schematically illustrates time-based management of dependent delete operations and replication between shards in a sharded object storage system.

FIG. 11 illustrates an example method for using barrier objects in data transfers.

FIG. 12 illustrates another example method for using barrier objects in data transfers.

FIG. 13 illustrated an example method for using barriers to manage dependent data operations among shards in a sharded object storage system.

FIG. 14 illustrates an example method for managing dependent delete operations.

FIG. 15 illustrates an example method for managing dependent delete operations for periodically generated dependent data objects.

DETAILED DESCRIPTION

FIG. 1 shows an embodiment of an example distributed storage system 1. In some embodiments, the distributed storage system 1 may be implemented as a distributed object storage system which is coupled to one or more clients 10.1-10.n for accessing data objects through one or more access nodes 20.1-10.n. The connection between the distributed storage system 1 and clients 10 could, for example, be implemented as a suitable data communication network. Clients 10 may host or interface with one or more applications that use data stored in distributed storage system 1. Such an application could, for example, be a dedicated software application running on a client computing device, such as a personal computer, a laptop, a wireless telephone, a personal digital assistant or any other type of communication device that is able to interface directly with the distributed storage system 1. However, according to alternative embodiments, the applications could, for example, comprise a suitable file system which enables a general purpose software application to interface with the distributed storage system 1, an application programming interface (API) library for the distributed storage system 1, etc. In some embodiments, access nodes 20 may include a file interface system for receiving file data requests from clients 10 according to a file system protocol and access data in storage nodes 30.1-30.40 using a different storage protocol, such as an object storage protocol.

As further shown in FIG. 1 , the distributed storage system 1 comprises a plurality of access nodes 20 and a plurality of storage nodes 30 which may be coupled in a suitable way for transferring data, for example by means of a conventional data communication network such as a local area network (LAN), a wide area network (WAN), a telephone network, such as the public switched telephone network (PSTN), an intranet, the internet, or any other suitable communication network or combination of communication networks. Access nodes 20, storage nodes 30 and the computing devices comprising clients 10 may connect to the data communication network by means of suitable wired, wireless, optical, etc. network connections or any suitable combination of such network connections. Although the embodiment of FIG. 1 shows only three access nodes 20 and forty storage nodes 30, according to alternative embodiments the distributed storage system 1 could comprise any other suitable number of storage nodes 30 and, for example, two, three or more access nodes 20 coupled to these storage nodes 30.

These access nodes 20 and storage nodes 30 may be built as general-purpose computers. Alternatively, they may be physically adapted for arrangement in large data centers, where they are arranged in modular racks 40.1-40.n comprising standard dimensions. Exemplary access nodes 20 and storage nodes 30 may be dimensioned to take up a single unit of such racks 40, which is generally referred to as 1U. Such an exemplary storage node may use a low-power processor and may be equipped with ten or twelve high capacity serial advanced technology attachment (SATA) disk drives and is connectable to the network over redundant Ethernet network interfaces. An exemplary access node 20 may comprise high-performance servers and provide network access to clients 10 over multiple high bandwidth Ethernet network interfaces. Data can be transferred between clients 10 and such access nodes 20 by means of a variety of network protocols including hypertext transfer protocol (HTTP)/representational state transfer (REST) object interfaces, language-specific interfaces such as Microsoft .Net, Python or C, etc. Additionally, such access nodes may comprise additional high bandwidth Ethernet ports to interface with the storage nodes 30. In some embodiments, HTTP/REST protocols complying with the Amazon Simple Storage Service (S3) object storage service may enable data transfer through a REST application protocol interfaces (API). Such access nodes 20 may operate as a highly available cluster of controller nodes with one or more integrated and/or independent interface systems, and provide for example shared access to the storage nodes 30, metadata caching, protection of metadata, etc.

As shown in FIG. 1 several storage nodes 30 can be grouped together, for example because they are housed in a single rack 40. For example, storage nodes 30.1-30.4 and 30.37-30.40 each are respectively grouped into racks 40.1 and 40.n. Access nodes 20 may be located in the same or different racks as the storage nodes to which the access nodes connect. A rack may have multiple access nodes, for example rack 40.1, a single access node as rack 40.n, or no access nodes (not shown) and rely on an access node in another rack or storage nodes or clients with built-in access node and/or controller node capabilities. These racks are not required to be located at the same location. They are often geographically dispersed across different data centers, such as for example rack 40.1 can be located at a data center in Europe and 40.n at a data center in the USA.

FIG. 2 is a block diagram of an example storage network 50 using a client architecture. In some embodiments, distributed storage system 1 may be embodied in such a storage network 50. As shown, storage network 50 can include multiple client devices 60 capable of being coupled to and in communication with a storage network 50 via a wired and/or wireless network 70 (e.g., public and/or private computer networks in any number and/or configuration (e.g., the Internet, an intranet, a cloud network, etc.)), among other examples that may include one client device 60.1 or two or more client devices 60 (e.g., is not limited to three client devices 60.1-60.3).

A client device 60 can be any computing hardware and/or software (e.g., a thick client, a thin client, or hybrid thereof) capable of accessing storage system 80 utilizing network 70. Each client device 60, as part of its respective operation, relies on sending input/output (I/O) requests to storage system 80 to write data, read data, and/or modify data. Specifically, each client device 60 can transmit I/O requests to read, write, store, communicate, propagate, and/or transport instructions, data, computer programs, software, code, routines, etc., to storage system 80. Client device(s) 60 and storage system 80 may comprise at least a portion of a client-server model. In general, storage system 80 can be accessed by client device(s) 60 and/or communication with storage system 80 can be initiated by client device(s) 60 through a network socket (not shown) utilizing one or more inter-process networking techniques. In some embodiments, client devices 60 may access one or more applications to use or manage a distributed storage system, such as distributed storage system 1 in FIG. 1 .

FIG. 3 shows a schematic representation of an embodiment of one of the storage nodes 30. Storage node 30.1 may comprise a bus 310, a processor 320, a local memory 330, one or more optional input units 340, one or more optional output units 350, a communication interface 360, a storage element interface 370, and two or more storage elements 300.1-300.10. Bus 310 may include one or more conductors that permit communication among the components of storage node 30.1. Processor 320 may include any type of conventional processor or microprocessor that interprets and executes instructions. Local memory 330 may include a random access memory (RAM) or another type of dynamic storage device that stores information and instructions for execution by processor 320 and/or a read only memory (ROM) or another type of static storage device that stores static information and instructions for use by processor 320. Input unit 340 may include one or more conventional mechanisms that permit an operator to input information to the storage node 30.1, such as a keyboard, a mouse, a pen, voice recognition and/or biometric mechanisms, etc. Output unit 350 may include one or more conventional mechanisms that output information to the operator, such as a display, a printer, a speaker, etc. Communication interface 360 may include any transceiver-like mechanism that enables storage node 30.1 to communicate with other devices and/or systems, for example mechanisms for communicating with other storage nodes 30 or access nodes 20 such as for example two 1 gigabit (Gb) Ethernet interfaces.

Storage element interface 370 may comprise a storage interface such as for example a SATA interface or a small computer system interface (SCSI) for connecting bus 310 to one or more storage elements 300, such as one or more local disks, for example 3 terabyte (TB) SATA disk drives, and control the reading and writing of data to/from these storage elements 300. In one exemplary embodiment as shown in FIG. 2 , such a storage node 30.1 could comprise ten or twelve 3 TB SATA disk drives as storage elements 300.1-300.10 and in this way storage node 30.1 would provide a storage capacity of 30 TB or 36 TB to the distributed storage system 1. According to the exemplary embodiment of FIG. 1 and in the event that storage nodes 30.2-30.40 are identical to storage node 30.1 and each comprise a storage capacity of 36 TB, the distributed storage system 1 would then have a total storage capacity of 1440 TB.

As is clear from FIGS. 1 and 3 the distributed storage system 1 comprises a plurality of storage elements 300. As will be described in further detail below, the storage elements 300, could also be referred to as redundant storage elements 300 as the data is stored on these storage elements 300 such that none or a specific portion of the individual storage elements 300 on its own is critical for the functioning of the distributed storage system. Each of the storage nodes 30 may comprise a share of these storage elements 300.

As shown in FIG. 3 storage node 30.1 comprises ten storage elements 300.1-300.10. Other storage nodes 30 in FIG. 1 could comprise a similar amount of storage elements 300, but this is, however, not essential. Storage node 30.2 could, for example, comprise six storage elements 300.11-300.16, and storage node 30.3 could, for example, comprise four storage elements 300.17-300.20. As will be explained in further detail below, the distributed storage system 1 may be operable as a distributed object storage system to store and retrieve a data object comprising data (e.g., 64 megabytes (MB) of binary data) and a data object identifier for addressing this data object, for example, a universally unique identifier such as a globally unique identifier (GUID). Embodiments of the distributed storage system 1 may operate as a distributed object storage system. Storing the data offered for storage by the application in the form of a data object, also referred to as object storage, may have specific advantages over other storage schemes such as block-based storage or file-based storage.

The storage elements 300 or a portion thereof may be redundant and operate independently of one another. This means that if one particular storage element 300 fails its function it can easily be taken on by another storage element 300 in the distributed storage system 1. However, as will be explained in more detail further below, the storage elements 300 are capable of providing redundancy without having to work in synchronism, as is for example the case in many well-known redundant array of independent disks (RAID) configurations, which sometimes even require disk spindle rotation to be synchronized. Furthermore, the independent and redundant operation of the storage elements 300 may allow a suitable mix of types of storage elements 300 to be used in a particular distributed storage system 1. It is possible to use for example storage elements 300 with differing storage capacity, storage elements 300 of differing manufacturers, using different hardware technology such as for example conventional hard disks and solid state storage elements, using different storage interfaces such as for example different revisions of SATA, parallel advanced technology attachment (PATA), and so on. This may result in advantages relating to scalability and flexibility of the distributed storage system 1 as it allows for adding or removing storage elements 300 without imposing specific requirements to their design in correlation to other storage elements 300 already in use in the distributed object storage system.

FIG. 4 shows a schematic representation of an embodiment of the access node 20. Access node 20 may include controller node functions and/or file system interface functions for client systems using file system protocols to access data stored in data objects in storage nodes 30. Access node 20 may comprise a bus 210, a processor 220, a local memory 230, one or more optional input units 240, one or more optional output units 250. In some embodiments, access node 20 may include object storage management functions, including object storage interface functions, version control management, and/or replication engines.

Bus 210 may include one or more conductors that permit communication among the components of access node 20. Processor 220 may include any type of conventional processor or microprocessor that interprets and executes instructions. Local memory 230 may include a random access memory (RAM) or another type of dynamic storage device that stores information and instructions for execution by processor 220 and/or a read only memory (ROM) or another type of static storage device that stores static information and instructions for use by processor 320 and/or any suitable storage element such as a hard disc or a solid state storage element. An optional input unit 240 may include one or more conventional mechanisms that permit an operator to input information to the access node 20 such as a keyboard, a mouse, a pen, voice recognition and/or biometric mechanisms, etc. Optional output unit 250 may include one or more conventional mechanisms that output information to the operator, such as a display, a printer, a speaker, etc. Communication interface 260 may include any transceiver-like mechanism that enables access node 20 to communicate with other devices and/or systems, for example mechanisms for communicating with other storage nodes 30 or access nodes 20 such as for example two 10 Gb Ethernet interfaces.

According to an alternative embodiment, the access node 20 could have an identical design as a storage node 30, or according to still a further alternative embodiment one of the storage nodes 30 of the distributed object storage system could perform both the function of an access node 20 and a storage node 30. According to still further embodiments, the components of the access node 20 as described in more detail below could be distributed amongst a plurality of access nodes 20 and/or storage nodes 30 in any suitable way. According to still a further embodiment, the clients 10 may run an access node 20. According to still further embodiments, access node 20 may be embodied in separate controller nodes and interface nodes with or without redundancy among the controller nodes and/or interface nodes.

FIG. 5 schematically shows selected modules of an access node or controller node with barrier objects, replication management, shard management, delete management, and supporting functions. Object storage system 500 may be configured as a node with an architecture and/or hardware similar to controller nodes and/or storage nodes. Object storage system 500 may incorporate elements and configurations similar to those shown in FIGS. 1-4 . For example, object storage system 500 may be configured in an access node 20 with object storage management functions.

Object storage system 500 may include a bus 510 interconnecting at least one communication unit 512, at least one processor 514, and at least one memory 516. Bus 510 may include one or more conductors that permit communication among the components of access system 500. Communication unit 512 may include any transceiver-like mechanism that enables access system 500 to communicate with other devices and/or systems. For example, communication unit 512 may include wired or wireless mechanisms for communicating with file system clients, other access systems, and/or one or more object storage systems or components, such as storage nodes or controller nodes. Processor 514 may include any type of processor or microprocessor that interprets and executes instructions. Memory 516 may include a random access memory (RAM) or another type of dynamic storage device that stores information and instructions for execution by processor 514 and/or a read only memory (ROM) or another type of static storage device that stores static information and instructions for use by processor 514 and/or any suitable storage element such as a hard disc or a solid state storage element.

Object storage system 500 may include or have access to one or more databases and/or specialized data stores, such metadata store 570 and object store 580. Databases may include one or more data structures for storing, retrieving, indexing, searching, filtering, etc. of structured and/or unstructured data elements. In some embodiments, metadata store 570 may be structured as reference data entries and/or data fields indexed by metadata key value entries related to data objects stores in object store 580. Object store 580 may include data objects comprised of object data (such as host data), some amount of metadata (stored as metadata tags), and a GUID. Metadata store 570, object store 580, and/or other databases or data structures may be maintained and managed in separate computing systems, such as storage nodes, with separate communication, processor, memory, and other computing resources and accessed by access system 500 through data access protocols. Metadata store 570 and object store 580 may be shared across multiple object storage systems 500.

In some embodiments, metadata store 570 and/or object store 580 may be sharded across multiple storage nodes. For example, object store 580 may include data objects and/or buckets of data objects that are managed across multiple storage nodes in redundant shards using replication functions to maintain substantial synchronization of available data objects. Object metadata and operations logs in metadata store 570 may be used to manage the redundant data shards and may themselves be sharded across multiple storage nodes.

Object storage system 500 may include a plurality of modules or subsystems that are stored and/or instantiated in memory 516 for execution by processor 514. For example, memory 516 may include a storage interface 520 configured to receive, process, and respond to object data requests from client systems or other nodes in distributed storage system 1. Memory 516 may include a barrier engine 530 configured to create and manage barrier objects in object store 580. Memory 516 may include a replication manager 540 configured to process data replication between storage nodes or storage systems, such as for transferring or replicating data objects to other object stores or across shards in the same object store. Memory 516 may include replication engines 548 for use by replication manager 540 in replicating data objects and related metadata among object storage systems and/or data shards. Memory 516 may include shard manager 550 for use by replication manager 540, delete manager 560, and/or other operation managers for managing the progress of log-based operations across shards. Memory 516 may include delete manager 560 configured to manage delete operations for object store 580 using metadata store 570. In some embodiments, barrier engine 530, replication manager 540, shard manager 550, and/or delete manager 560 may be integrated into storage interface 520 and/or managed as separate libraries or background processes (e.g., daemon) through an API or other interface.

Storage interface 520 may include an interface protocol or set of functions and parameters for storing, reading, and otherwise managing data requests to an associated distributed storage system and the corresponding metadata data source for mapping file data to persistent storage data elements. For example, storage interface 520 may include functions for reading, writing, modifying, deleting, or otherwise manipulating data objects and their respective client or host data and metadata in accordance with the protocols of an object storage system.

In some embodiments, storage interface 520 may include a plurality of hardware and/or software modules configured to use processor 514 and memory 516 to handle or manage defined operations of storage interface 520. For example, storage interface 520 may include a client request handler 522, a metadata manager 524, a storage manager 526, and a client response handler 528. For any given client request, storage interface 520 may receive a client request through client request handler 522 and determine one or more operations based on the content of the request. These operations may include metadata operations handled by metadata manager 524 and/or object data operations handled by storage manager 526. The results of these operations may be processed, formatted, and returned by client response handler 528.

Storage interface 520 may include one or more object storage configurations that define a storage protocol used for validating, managing, and processing object data requests. For example, object storage configurations may generally define the availability of version control for any given object or bucket, as well as specifying how the object storage system creates and manages versioning information. Object storage configurations may also define what metadata is collected and stored, as well as whether it is stored in a metadata database, such as metadata store 570, in object metadata tags stored in the data objects, and/or a combination thereof.

Client request handler 522 may include an interface and/or communication event-based condition for receiving object data requests from one or more clients. For example, client systems may send an object data request over a network connection and addressed to object storage system 500 or a port or component thereof. Client request handler 522 may receive these requests and parse them according to the appropriate communication and object storage protocols. For example, client request handler 522 may identify a transaction identifier, a client identifier, an object identifier (object name or GUID), a data operation, and additional parameters for the data operation, if any, from the received message or messages that make up the object data request.

Metadata manager 524 may include interfaces, functions, and/or parameters for creating, modifying, deleting, accessing, and/or otherwise managing object metadata, such as object metadata 572 stored in metadata store 570. For example, when a new object is written to object store 580, at least one new metadata entry may be created in metadata store 570 to represent parameters describing or related to the newly created object. Metadata manager 524 may generate and maintain an object data index for managing object metadata 572 and enabling metadata manager 524 to locate object metadata within metadata store 570. For example, metadata store 570 may be organized as a key-value store and the object data index may include key values for data objects.

In some embodiments, metadata store 570 may include one or more operation logs 574 for storing log operation entries for operations, such as write, read, delete, etc., targeting the data objects in object store 580. For example, operations related to data objects may be indexed with a key value that includes the object identifier or GUID for each object, an operation time value 574.1 (such as a timestamp or inverse timestamp) for when the operation occurred, and an operation type. Metadata manager 524 may generate operations time-based log entries for each data operation as it is processed and store them in timestamp order in metadata store 570. In some embodiments, metadata manager 524 may also manage object metadata stored in object store 580 with object data 582. Metadata manager 524 may work in conjunction with storage manager 526 to create, modify, delete, access or otherwise manage object metadata stored as object tags within object store 580.

Storage manager 526 may include interfaces, functions, and/or parameters for reading, writing, and deleting object data elements in object store 580. For example, object PUT commands may be configured to write an object identifier, object data 582, and/or object tags to object store 580. Object GET commands may be configured to read data from object store 580. Object DELETE commands may be configured to delete data from object store 580, or at least mark a data object for logical deletion until a future garbage collection or similar operation actually deletes the data or reallocates the physical storage location to another purpose.

Other object storage commands may be handled by storage manager 526. Object storage commands processed by storage manager 526 may include parameters for accessing special functions and/or metadata resources stored with host or client data in the data objects. Storage manager 526 may work in conjunction with metadata manager 524 and barrier engine 530 for generating and managing barrier objects. Storage manager 526 may work in conjunction with replication manager 540 to replicate or migrate data from object store 580 to another data store or among shards. For example, storage manager 526 may read the object store 580 for transfer by one or more replication engines 548 managed by replication manager 540.

Client response handler 528 may include an interface and/or communication logic for sending response messages, such as result, status, or error messages, to one or more clients related to object data requests received. For example, client response handler 528 may wait for processing by metadata manager 524 and/or storage manager 526 to complete or generate an error, and then provide an appropriate result or error message to the client system(s) for each object data request received.

In some embodiments, metadata store 570 may be distributed across multiple systems, such as a plurality of access systems. Metadata store 570 and/or portions thereof may be sharded data stores, wherein the data stores are partitioned into segments stored in different computing systems or storage nodes. Storage interface 520 may include the functions for locating and accessing relevant portions of the sharded database and/or use shard manager 550.

Barrier engine 530 may be invoked by storage interface 520 to manage barrier objects in object store 580. Barrier objects may include data objects that are designated as dependent data objects and may require a group of other data objects, the dependency set of data objects for that dependent data object, to be replicated or otherwise processed through a log-based operation prior to replicating or processing the dependent data object. For example, barrier objects may be created as part of a user application request, a system management application, and/or a periodic aggregation of data store or metadata contents (e.g., a snapshot) configured as a system operation. Barrier objects may be defined by their dependence on other data objects to be valid. If data objects in the dependency set are changed or missing, then the barrier object may reference wrong or missing objects that generate errors or failures in the applications using the barrier objects. For example, a snapshot of the contents of a data bucket may only be valid as long as the contents of that data bucket as of the time of the snapshot are present. This may have particular relevance in the context of replication and assuring that the barrier object remains valid in the destination object store. As discussed above, in some systems and applications, it may be prohibitive in time, processing capacity, and other resources to attempt to validate a replicated barrier object after the fact, such as by checking all dependencies through the operation log or scan of object metadata. Barrier engine 530 may operate in conjunction with replication manager 540 to assure that the dependency set for a barrier object is replicated to a destination object store prior to replicating the barrier object itself.

Barrier engine 530 may provide interfaces, functions, and parameters for managing the use of barrier objects or dependent data objects, such as snapshots of higher-level data structures within a data store, storage node, data bucket, or other grouping of data objects. Barrier objects may include references to all data objects in a node, bucket, or other logical structure, data object metadata, log information, databases, data structures pointing to a blobstore, or similar aggregations of information related to other data objects. In some embodiments, barrier engine 530 may be included within storage interface 520, such as within library functions used by client request handler 522, metadata manager 524, storage manager 526, and/or client response handler 528 for handling requests generating or otherwise related to barrier objects.

In some embodiments, barrier engine 530 may include one or more hardware and/or software modules or data structures for executing specific operations. For example, barrier engine 530 may include a dependency set identifier 532, a barrier object identifier 534, a barrier object generator 536, and a barrier release handler 538.

Dependency set identifier 532 may include one or more parameters, data structures, and/or related methods for manipulating those parameters or data structures. Dependency set identifier 532 may be configured to determine dependency sets of data objects for barrier objects in object store 580. In some embodiments, a barrier object may be received from a client application, another data store (e.g., as the destination for replication operations), and/or generated by barrier object generator 536. Dependency set identifier 532 may identify the dependency set for the barrier object and enable replication manager 540 to track or determine whether the dependency set has been replicated. For example, dependency set identifier 532 may provide dependency set parameters to replication manager 540 for each barrier object.

In some embodiments, the dependency set for a snapshot of data objects may be defined as a function of key entries, timestamps, bucket identifiers, or other identifiers that may be applied to object metadata 572 and/or operation logs 574 to determine whether the dependency set has been replicated or processed. Functions and/or parameters defining the dependency set in terms of an existing object list, such as metadata entries or operation log entries, may avoid maintenance of a separate list of dependency set data objects for each barrier object. In some embodiments, the dependency set may include a single object or identifiable group of objects, such as blobstore object, and dependency set identifier 532 may use one or more pointers or object identifiers. For example, the barrier object or related metadata may include a list of pointers or identifiers (e.g., GUIDs) used by dependency set identifier 532 to identify the dependency set. The dependency set of data objects may include one or more data objects and, in some implementations, may include millions of data objects for each barrier object.

Barrier object identifier 534 may include one or more parameters, data structures, and/or related methods for manipulating those parameters or data structures. Barrier object identifier 534 may be configured to identify barrier objects so that they can be delayed by replication manager 540 until their dependency set has been processed. In some embodiments, barrier object identifier 534 may include one or more barrier object identifier values associated with the barrier object. For example, the object name 534.1 may include one or more values, such as a string or extension (e.g., *.snapshot), that identifies data objects that include the barrier object identifier value as barrier objects. In other examples, the barrier object identifier value may be stored as object metadata value 534.2 in object metadata 572 or a metadata tag in object data 582.

In some embodiments, barrier object identifier 534 may use special commands or operations that designate barrier objects, such as dependency request 534.3. For example, barrier object identifier 534 may identify a data object as a barrier object in response to a put request or other data operation that includes an extension or argument identifying it as a dependency request 534.3 that designates the target data object as a barrier object. In some embodiments, dependency request 534.3 may include dependency set parameters for use by dependency set identifier 532. Barrier object identifier 534 may pass dependency request 534.3 to replication manager 540 to identify the barrier object and/or dependency set or may parse dependency request 534.3 and pass the barrier object identifier value and/or dependency set parameter values to replication manager 540.

In some embodiments, barrier object identifier 534 may use operation logs 574 to identify barrier objects using a log editor 534.4. For example, a barrier object identifier value, such as barrier indicator 574.2 in operations log 574, may be stored in an operation log entry for an operation on the barrier object (such as the object creation or put entry). In some embodiments, the key value for an operation log entry may be modified to include the barrier object identifier value, such as adding “.snapshot” to the key value. In some embodiments, log editor 534.4 may be configured to include barrier indicator 574.2, such as a barrier object identifier value, in object name 534.1, as a special operation type (e.g., based on dependency request 534.3), or in an extension or other log entry data.

Barrier object generator 536 may include functions, methods, or operations for generating barrier objects. For example, responsive to a client request, system call, or an internal logic for triggering barrier objects, barrier object generator 536 may generate a snapshot of a target node, bucket, or other logical group for use as a barrier object for the dependency set it is generated from. In some embodiments, barrier object generator 536 may respond to a special command, such as a client or host request for a barrier object, or other operation for initiating the generation of a barrier object. For example, storage interface 520 may support one or more snapshot operations and requests specifying a target object store, node, bucket, or other group of data objects and may cause barrier object generator 536 to create a corresponding barrier object for the specified dependency set. In some embodiments, storage interface 520 may support one or more other operations for generating barrier objects for other types of dependent data objects, such as log or metadata snapshots, database or blobstore reference capture, etc., and may invoke barrier engine 530 and/or barrier object generator 536 to generate the desired barrier object based on parameters in the request and one or more object generation functions.

In some embodiments, barrier object generator 536 may be configured to generate barrier objects automatically to support system management and/or application functions. For example, barrier object generator 536 may be configured to generate a snapshot at predetermined intervals during operation of object data store 500. In some embodiments, the predetermined intervals may be implemented through an interval timer 536.1. For example, interval timer 536.1 may be configured for a predetermined time interval, such as every 5 minutes, and use a clock function to monitor the elapsed time in the interval to initiate the next barrier object generation. In some embodiments, interval timer 536.1 may interact with one or more additional logical rules for determining whether and when a barrier object is generated. For example, reaching the time interval may trigger one or more additional assessments of state, such as whether or not replication of a prior barrier object has completed, to determine whether the barrier object generation should be initiated or more time should be added to the interval to allow the other trigger conditions to be completed.

Barrier release handler 538 may include functions, methods, or operations for responding to completion of an operation that releases the barrier object. For example, once replication of the dependency set has been completed, the barrier object may be released from the delay or hold that kept it from being processed through the operation. In some embodiments, an operation manager, such as replication manager 540, may include logic for delaying processing of the barrier object and may automatically release the barrier object and process it responsive to completion of the dependency set. In some embodiments, barrier release handler 538 may monitor the progress of the operation and determine when the dependency set has completed in order to release the barrier object for processing. In some embodiments, barrier release handler 538 may generate or trigger additional operations responsive to releasing the barrier object. For example, barrier release may trigger barrier object generator 536 to generate a next barrier object or trigger a confirmation message to a client application or system utility regarding release of the barrier object.

Replication manager 540 may include functions, methods, or operations for managing the transfer of data objects to other object stores or object storage systems. For example, replication manager 540 may manage one or more replication engines 548 that move data objects from object store 580 to a destination object store that may be remote from object store 580. In some embodiments, replication manager 540 may transfers each data object, including object data 582 and any associated object tags. In some embodiments, replication manager 540 may also transfer metadata associated with the data object from a corresponding metadata store, such as metadata store 570, including object metadata 572.

In some embodiments, replication manager 540 may be configured for asynchronous data transfers using a plurality of replication engines 548. For example, a scheduler may select objects for transfer based on criteria other than the order in which they were ordered within object store 580, such as object size, availability, etc. In some embodiments, replication engines 548.1-548.n may operate in parallel and transfer data objects, including data objects in one or more dependency sets, at similar times and using resources and paths that may result in different transfer rates. Thus, the data objects may be ingested at the destination system in a different order than they were stored in object store 580 or even a different order than they were sent. In some embodiments, replication manager 540 may manage a dedicated set of replication engines or a dynamically allocated subset of replication engines shared with one or more other replication managers. Replication manager 540 may also support multipart data object transfers that include a plurality of write transactions to complete a single object replication. For example, a multipart write process may include a series of commands or messages, such as INIT, multiple PARTPUT, and COMPLETE functions.

In some embodiments, replication manager 540 may include one or more hardware and/or software modules or data structures for executing specific operations. For example, replication manager 540 may include a replication queue 542, barrier delay logic 544, and retry/error logic 546.

Replication queue 542 may include one or more parameters, data structures, and/or related methods for organizing data objects identified for replication. For example, data objects may be identified from a command, request, or background operation for replication to a destination data store or shard and added to a data structure embodying replication queue 542. In some embodiments, replication queue 542 may be generated by parsing operation logs 574 in order of operation time 574.1, such as in order of the timestamps associated with each operation log entry. In some embodiments, replication queue 542 may be ordered or reordered based on logic for enforcing service levels, load balancing across replication engines 548, or other efficiency or priority considerations. Replication queue 542 may support parallel replication by replication engines 548 and may include multiple queues and/or selection logic for determining which replication selects one or more data objects from replication queue 542.

Barrier delay logic 544 may include functions, methods, or operations for enforcing the delayed transfer of barrier objects to ensure that all data objects in the corresponding dependency set have been replicated to the destination data store. For example, barrier delay logic 544 may identify barrier objects in replication queue 542 (and/or prior to being added to replication queue 542) and place them in a hold register or hold queue to prevent them from being passed to replication engines 548. In some embodiments, barrier delay logic 544 may place a flag in the entry for a held barrier object in replication queue 542 that indicates that the barrier object should not be selected or assigned to a replication engine until the barrier object is released and the flag is removed. In some embodiments, barrier indicator 574.2 may identify the barrier objects that should be delayed and barrier delay logic 544 may skip such entries in operations log 574 and proceed to data operation entries with a later operation time 574.1 and corresponding timestamp value.

Retry/error logic 546 may include a data structure and related functions for maintaining a list of failed replication requests that may be retried through an iterative retry process. For example, replication errors, such as failure of a replication engine and/or destination data store to confirm successful storage at the destination data store, may trigger an iterative retry process for a fixed number of retries, retry period, or other limit on retry attempts. In some embodiments, replication queue 542 may include a retry queue, such as an operations table, with entries for each pending replication request and one or more parameters for determining priority, number of attempts, and other replication data relevant to managing retry attempts. In some embodiments, retry/error logic 546 may include a barrier object failure condition triggered when the retry attempts fail and either the barrier object or any dependency set object cannot be verified as successfully replicated to the destination data store. For example, if one or more dependency data objects remain in the retry/error queue for a predetermined period or number of attempts, retry/error logic 546 may generate a replication failure error for the barrier object that may be communicated to a client application, such as through client response handler 528, or to a system administrator or another system utility.

Shard manager 550 may include functions, methods, or operations for managing the log-based operations among a plurality of shards, such as shards configured to mirror their contents. For example, object storage system 500 may configure object store 580 to be managed in a plurality of redundant shards and distribute data operations, such as client application requests and background management operations, among the shards. Shard manager 550 may operate in conjunction with replication manager 540, barrier engine 530, and/or storage interface 520 to ensure that log-based operations are managed across the shards to maintain the validity of dependent data objects and data operations targeting them. For example, replication manager 540 may be responsible for processing operations log 574 in each shard to identify data objects for replication to other shards and may use shard manager 550 for coordinating among the shards, particularly with regard to dependent data objects and their dependency sets. Other background operations executed against operations log 574, such as delete processing, garbage collection, aggregation of storage parameters, etc., may also rely on shard manager 550 to assure valid dependencies in each shard being operated on.

In some embodiments, shard manager 550 may coordinate among local resources and each shard may include instances of replication manager 540, barrier engine 530, and/or storage interface 520. For example, shard manager 550 may itself be a sharded data structure in metadata store 570 and associated methods or functions to utilize those data structures. In some embodiments, shard manager 550 may include one or more hardware and/or software modules or data structures for executing specific operations. For example, shard manager 550 may include a shard identifier 552, log progress monitor 554, and shard progress checker 556.

Shard identifier 552 may include one or more parameters, data structures, and/or related methods for identifying a plurality of shards that are related for one or more dependent data operations. For example, two or more shards may be configured to mirror the data objects stored in each and shard identifier 552 may include a table or configuration file that identifies the location, scope, and relationships among those shards. In some embodiments, shards may be configured at the data bucket level and a bucket configuration file may include the number and location of shards across which the data bucket is replicated. In some embodiments, shard identifier 552 may use a shard identifier value to identify and address shards and their respective storage nodes. For example, the configuration file may identify the storage node or other shard identifier value and shard identifier 552 may provide the shard identifier values to replication manager 540 for replicating data objects among the identified shards. In some embodiments, shard manager 550 may be instantiated for each shard and shard identifier 552 may include or access a shard lookup table for identifying the shards related to the shard corresponding to that instance of shard manager 550.

Log progress monitor 554 may include one or more parameters, data structures, and/or related methods for monitoring the progress of one or more data operations in a plurality of operation logs, such as the operations logs (or sets of operations log entries) corresponding to each shard in operations log 574. In some embodiments, where the relevant log-based operation proceeds in timestamp order through the shard operations log, log progress monitor 554 may monitor a progress value corresponding to the most recent timestamp value processed through the operation. For example, replication manager 540 may add data objects to replication queue 542 sequentially by proceeding in time order through the operation log entries and identifying object creation or modification entries for objects that have not yet been replicated. The progress value may correspond to the highest or most recent time value that has been processed by replication manager 540. In some embodiments, log progress monitor 554 may track progress vectors for each operation in each object store shard as they proceed through their respective operations logs.

In some embodiments, log progress monitor 554 may monitor a progress value for each dependent data object in each shard, such that the number of progress values being monitored at any given time may be the number of dependent data objects times the number of shards. For example, log progress monitor 554 may manage a table including a matrix defined by shard identifier values and dependent data object identifier values and including progress values for each combination thereof. In some embodiments, the progress values may be based on log operation entry timestamps that reflect the local time of that operation in that shard and may not directly correlate to timestamps or progress values in other shards.

Shard progress checker 556 may compare the progress value for replication in each shard against timestamp values of the operation log entries for the barrier objects or other barrier indicators in each of those shards. So, the progress value for replication in a first shard may be compared against the timestamp value of the operation log entry with a barrier indicator in the operation log of that first shard, the progress value for replication in a second shard may be compared the timestamp value of the operation log entry with a barrier indicator in the operation log of that second shard, and so on. Each barrier indicator being compared may correspond to the same barrier object that is being replicated across the shards.

In some embodiments, shard progress checker 556 may only return an indicator that replication of the dependency data set is complete for a barrier object when the progress indicator for each shard has exceeded the barrier indicator in that shard and this is true in all shards involved in the replication. A similar progress condition may be checked for other log-based operations to ensure that the dependency data set is present in all of the shards prior to releasing any of the barrier objects.

Delete manager 560 may include functions, methods, or operations for managing the log-based delete operations. For example, object storage system 500 may configure metadata store 570 for logical delete operations in response to client delete commands received through storage interface 520 and asynchronous physical delete operations managed by a background garbage collection operation. Delete manager 560 may be configured to handle delete processing in object storage systems supporting dependent data objects. For example, barrier engine 530 may periodically generate dependent data objects, replication manager 540 may manage the replication of dependency data sets prior to replicating the dependent data objects to assure that dependencies are in place when the dependent data object is transferred, and delete manager 560 may delete dependent data objects prior to deleting the dependency data objects to assure that the dependent data object is not rendered invalid due to deletion of objects in its underlying dependency set. In some embodiments, replication of delete operations may be managed by delete manager 560 in conjunction with replication manager 540 to assure that dependent data object deletes are replicated prior to replicating any dependency set data object deletes, since deletion of a dependency set data object prior to deleting the dependent data object may render the dependent data object invalid in the destination data store.

In some embodiments, delete manager 560 may be configured to pre-process delete commands received through storage interface 520 and/or generated by another system component prior to executing the delete process for the target data object. For example, delete manager 560 may sort delete requests into dependent data objects and independent data objects (which may generally include dependency set data objects that are not themselves dependent data objects). Independent data objects may also include data objects that have no relation to dependent data objects if the system supports such unrelated objects. In some embodiments, the sorting of delete operations into dependent and independent lists may enable dependent data objects and their dependency sets to be deleted (and those delete operations replicated) without specifically identifying which dependency set data objects are associated with a particular dependent data object. For example, in a system that generates and deletes snapshots on a recurring basis, such as described above regarding barrier object generator 536, the snapshot delete operations may be managed using the dependent data object list and all other delete operations may be managed using the other list. Delete manager 560 may coordinate the timing of processing delete requests based on whether the target objects are dependent or independent data objects and their timestamps, assuring the deletion of barrier objects prior to any dependency set data objects they reference. In some embodiments, delete requests for independent data objects may be received and delete manager 560 may determine or identify that the independent data objects are in one or more dependency sets. In order to delete the data objects from the dependency data set, delete manager 560 may generate the delete request for the dependent data object or objects that would be rendered invalid by the deletion of the dependency data object.

Delete manager 560 may operate in conjunction with shard manager 550, replication manager 540, barrier engine 530, and/or storage interface 520 to ensure that log-based delete operations are managed across the shards to maintain the validity of dependent data objects and data operations targeting them. In some embodiments, delete manager 560 may include one or more hardware and/or software modules or data structures for executing specific operations. For example, delete manager 560 may include a delete processor 562, dependent queue 564, independent queue 566, and interval priority logic 568.

Delete processor 562 may include functions, methods, or operations for deleting data objects in object store 580. For example, delete processor 562 may include functions for logically deleting data objects using metadata store 570 to render deleted data object inaccessible to normal access commands, such as storage requests originating from client applications using object storage system 500. Once a data object is selected for deletion by delete manager 560, delete processor 562 may log a delete operation entry 574.3 that includes a delete marker rendering the referenced data object no longer accessible to other storage operations. In some embodiments, delete processor 562 may communicate with replication manager 540 and enable data objects that have been delete processed by delete processor 562 to have the delete operation replicated to one or more other data stores containing the data objects. In some embodiments, delete manager 560 may use dependent queue 564, independent queue 566, and interval priority logic 568 to determine what set of data objects with pending delete requests are eligible for selection and deletion by delete processor 562.

Dependent queue 564 and independent queue 566 may each include one or more parameters, data structures, and/or related methods for identifying dependent data object and independent data objects (respectively) for delete operations. For example, delete manager 560 may sort data objects identified for deletion between dependent queue 564 as a dependent operations queue and independent queue 566 as an independent operations queue. Data objects with dependent object identifiers, such as barrier indicator 574.2, may be added to dependent queue 564 and other data objects (without dependent object identifiers), which may include data object identified as dependency set data objects supporting dependent data objects, may be added to independent queue 566. In some embodiments, dependent queue 564 and independent queue 566 may include object identifiers and/or operations log key values for each data object that has received a delete request and they may be organized in one or more data structures to be processed by delete processor 562 according interval priority logic 568. Delete requests for dependent data objects based on dependent queue 564 may be referred to as dependent delete requests and delete requests for dependency set data objects based on independent queue 564 may be referred to as dependency delete requests or dependency set delete requests.

Interval priority logic 568 may include functions, methods, or operations for selecting data objects for deletion from dependent queue 564 and independent queue 566 for deletion. For example, interval priority logic 568 may give priority to dependent queue 564 until it can guaranty that one or more dependent data objects have been deleted prior to allowing any independent data object in their dependency sets to be deleted. In some embodiments, interval priority logic 568 may be associated with and/or based on interval timer 536.1 and barrier object generator 536 to manage the deletion of snapshots on a regular basis, such as a predetermined snapshot intervals. For example, interval priority logic 568 may identify snapshot generation events and the intervals between such generation events to determine which dependent data objects and independent data objects may be deleted in each such interval based on the data object timestamps. Example interval-based priority logic based on periodic snapshots as dependent data objects may be further described below with regard to FIGS. 9 and 10 . In some embodiments, delete processing may also be coordinated on an interval basis with replication of delete operation entries.

Memory 516 may include additional logic and other resources (not shown) for processing object data requests, such as modules for generating, queueing, and otherwise managing object data requests. Processing of an object data request by storage interface 520 may include any number of intermediate steps that yield at least one data request to the distributed storage system.

FIG. 6 shows data objects 634 transferred among example object storage systems 630.1-630.n in a distributed storage system 600 using barrier objects 636. In some embodiments, object storage systems 630 may be configured according to the systems and elements described with regard to FIGS. 1-5 . In the example shown, a series of data objects 634 may be replicated from object storage system 630.1 to 630.2 using replication engines 640 and using barrier objects 636 to assure that dependency sets 638 of data objects 634 are completed before the barrier objects 636 themselves are transferred. While the transfer is shown occurring between object storage system 630.1 and 630.2, the same replication process may be carried out with any number of object storage systems 630.n−1 and 630.n. Similarly, each object storage system 630.2-630.n may similarly include replication engines 640 for replicating objects from their respective object stores 632 to the other object storage systems 630 using a similar process.

In the example shown, data objects 634.1.1, 634.1.2, and 634.1.3 may form a dependency set 638.1.1 for barrier object 636.1.1. Data objects 634.1.4 and 634.1.5 may form dependency set 638.1.2 for barrier object 636.1.2. Data objects 634.1.6 and 634.1.7 may form dependency set 638.1.n for barrier object 636.1.n. The order shown for object store 632.1 may be the order in which data objects 634 were received and stored and the barrier objects 636 were generated and stored in object store 632.1. However, their replication order 620 may be different due to the efficient use of parallel replication, different network storage paths, temporary errors, or other variations. For example, data object 634.1.3 may be the first data object replicated and data object 634.1.7, from dependency set 638.1.n, may be the second data object replicated. Object storage system 630.1 may, as described above, delay each barrier object 636 until it confirms that the dependency set 638 for that barrier object has been replicated.

As a result, object storage system 630.1 does not send barrier object 636.1.1 until data object 634.1.1 completes replication of dependency set 638.1.1, which includes data objects 634.1.1, 634.1.2, and 634.1.3. The same is true for each other barrier object 636.1.2 and 636.1.n. One or more data objects 634 may be sent out of sequence and/or ahead of a prior barrier object completing replication. The trigger condition for complete replication of the dependency set that allows for replication of the corresponding barrier object is replication of each data object in the replication set, regardless of order. Object storage system 630.1 does not send barrier object 636.1.2 until data object 634.1.5 completes replication of dependency set 638.1.2 and does not send barrier object 636.1.n until data object 634.1.6 completed replication of dependency set 638.1.n. In some embodiments, the order of barrier objects 636 may be maintained even though dependency set 638.1.n completed before dependency set 638.1.2.

Object storage system 630.2 may store the data objects and barrier objects in the order they were received. For example, data objects 634.2.3, 634.2.7, 634.2.2, and 634.2.1 may be stored before barrier object 636.2.1 and data objects 634.2.4, 634.2.6, and 634.2.5 may be stored before barrier objects 636.2.2 and 636.2.n. Client application requests and background operations targeting barrier objects 636 in object storage system 630.2 may therefore rely on barrier objects 636 to continue to be supported by their dependency sets 638 as soon as the barrier objects 636 appear on object storage system 630.2.

FIG. 7 shows a sharded object storage system 700 where data objects are being replicated across data store shards 710.1-710.n. In some embodiments, object storage system 700 may be configured according to the systems and elements described with regard to FIGS. 1-5 . Each shard 710 may have a corresponding operation log 720.1-720.n. In the example shown, operation logs 720 include timestamps 722 and object identifiers 724.

Operations in the time-based operation log 720.1 may proceed along timestamps 722.1 from T1 to Tn. The group of objects 726 may represent a related group of data objects A, J, B, and C. A replication barrier 728 may have been set at time 3 to generate or identify data object C as barrier object 730. As described above, once data object C is designated barrier object 730 and the remaining data objects in group of objects 726 are identified as the dependency set preceding replication barrier 728, data objects A, J, and B may be replicated in any order and data object C will only be transferred once transfer of the others is complete.

Shard 720.2 may be operating at the same time as shard 720.1, but operation log 720.2 may record data operations on shard B based on local timestamps 722.2 (i.e. T1 in Log A may not be the same as T1 in Log B). As shown, shard B may receive data objects, such as X and Y, from other sources, such as client applications or other shards, in addition to objects J, A, and B being replicated from shard A. Shard 720.2 may receive the dependency set of data objects J, A, and B in a different order than they were created or appeared in operation log 720.1, but all three must be received before data object C, barrier object 730, may be replicated. As shown, data object B is the last in the dependency set received by shard B at T3 and data object C is received at T6.

FIGS. 8A-8D shows a sharded object storage system 800 where data objects are replicated across data store shards 810.1-810.n. In some embodiments, object storage system 800 may be configured according to the systems and elements described with regard to FIGS. 1-5 . Each shard 810 may have a corresponding operations logs 820.1-820.n. In the example shown, operation logs 820 include operation log entry identifiers 822.1-822.n, timestamps 824.1-824.n, and object identifiers 826.1-826.n for each operation log entry. In some embodiments, log entry identifiers 822 for each operation log entry may correspond to a key value that includes timestamps 824, object identifiers 826, and/or additional log entry parameter values, such as an operation type and/or one or more status values. As shown, operation log entries in operation logs 820 may be in time-based order from an oldest operation timestamp (e.g., T1) to a most recent operation timestamp (e.g., Tn). In some embodiments, operation log entries may use inverse timestamps and/or be organized in reverse chronological order.

In FIG. 8A, an initial timepoint is shown for a log-based operation relying on dependent data objects, such as snapshots being replicated among shards 810. In the example shown, barrier entries 828.1 and 828.2 may be identified in operations logs 820.1 and 820.2 respectively, corresponding to objects 99 and 80 in their respective logs and including barrier timestamp values (e.g., T99 and T80). In some embodiments, barrier entries 828.1 and 828.2 may be related snapshots generated by the same barrier object generation event. The object entries among the operation log entries preceding barrier entries 828.1 and 828.2 may be determined to be the dependency set of data objects and corresponding operation log entries for their respective barrier entries 828.1 and 828.2. At this first time in the operation, progress values 830.1.1 and 830.2.1 are early in the dependency set of operation log entries for their respective operations logs 820 and neither has exceeded their respective barrier entries 828.1 and 828.2. Barrier entries 828.1 and 828.2 may be include barrier indicators that will delay processing of those entries until barrier release conditions are met.

In FIG. 8B, a second timepoint is shown for the log-based operation as it proceeds through the operation log entries in direction of processing 832. Progress values 830.1.2 and 830.2.2 may have progressed in the dependency set of the operations log entries for their respective operations logs 820, but neither has exceeded their respective barrier entries 828.1 and 828.2. Barrier release conditions may not have been met and barrier entries 828.1 and 828.2 may still be held.

In FIG. 8C, a third timepoint is shown for the log-based operation as it proceeds through the operation log entries in direction of processing 832. Progress values 830.1.3 and 830.2.3 may have progressed in the dependency set of the operations log entries for their respective operations logs 820. Progress value 830.1.3 has exceeded barrier entry 828.1, but the barrier may not have been released because the release condition may require that all shards have exceeded their respective barrier entries and progress value 830.2.3 has not exceeded barrier entry 828.2. Note that progress through operations logs 820 may proceed at very different paces, depending on the available resources and other conditions at the respective storage nodes hosting the shards. Because progress value 830.2.3 has not exceeded barrier entry 828.2, neither of barrier entries 828.1 and 828.2 may be released and the hold or delay on barrier entry 828.1 causes processing of the dependent data object in barrier entry 828.1 to be skipped. Processing may continue to operation log entries with later timestamp values than barrier entry 828.1 in operations log 820.1.

In FIG. 8D, a fourth timepoint is shown for the log-based operation as it proceeds through the operation log entries in direction of processing 832. Progress values 830.1.4 and 830.2.4 may have progressed in the dependency set of the operations log entries for their respective operations logs 820. Progress values 830.1.4 and 830.2.4 have exceeded their respective barrier entries (formerly barrier entries 828.1 and 828.2 in FIGS. 8A-8C). Because the release condition requiring that all shards have exceeded their respective barrier entries has been met, these barrier entries have been released and may no longer contain barrier indicators. The operations log entries that were formerly barrier entries are no longer delayed or held and may be processed by the operation, such as being replicated to another shard. In some embodiments, release of the barrier objects may be a trigger condition for the generation of a next iteration of barrier object generation.

FIG. 9 shows data object delete requests being processed and replicated to other storage systems in an object storage system 900 using markers, snapshots, or barrier objects. In some embodiments, object storage system 900 may be configured according to the systems and elements described with regard to FIGS. 1-5 . For example, object storage system 900 may be configured to generate snapshots at predetermined time intervals to mark the state of a source bucket or group of related data objects. A source system may receive or generate dependent delete requests (M1, M2, Mn) 922, 932, 952 and related dependency set delete requests (A, B, C, D, E, F) 924, 934, 954. Object storage system 900 may be configured to use times (T0, T1, T2) 920, 930, 950 and the time intervals between them to assure the ordered processing of dependent delete requests 922, 932, 952 ahead of their respective dependency set delete requests 924, 934, 954. A timeline 910 shows the flow of data object delete requests for the independent or dependency set data objects A, B, C, D, E, and F between the times where the dependent delete requests 922, 932, and 952 are generated. The resulting flow of delete requests, delete processing/replication, and delays in each processing interval is shown between times 920, 930, 950, and 970 proceeding from left to right.

At time (T0) 920, a first barrier object delete request (M1) 922 may be generated for a previously generated dependent data object that directly or indirectly references data objects A, B, and C, (which are therefore members of the dependency set for barrier object M1). An operating period may pass between time (T0) 920 and time (T1) 930. During that time interval, delete requests 924 may be received for data objects A, B, and C. Delete request 922 may be processed 926 for M1 in order to remove the dependent data object prior to processing the delete requests 924 for the dependent data objects. In some embodiments, this dependent delete operation may occur without specifically determining that data objects A, B, and C are dependency set data objects for M1 and the process may apply for all data objects or all data objects in a logical group related to the snapshot, such as a bucket. Delete processing of data objects A, B, and C may be delayed 928 during the time interval between time (T0) 920 and time (T1) 930. Again, this may not be based on identifying data objects A, B, and C in the dependency set, but processing all delete operations that are not themselves dependent data objects with an interval delay. In some embodiments, delete requests A, B, C may be sorted into a first list, such as an independent delete queue, and the delete request for M1 may be sorted into a second list, such as a dependent delete queue, with the dependent delete queue receiving priority for processing while the independent delete queue may only be processed for delete requests received prior to T0 until after T1, resulting in delay 928.

At time (T1) 930, a second barrier object delete request (M2) 932 may be generated for a previously generated dependent data object that directly or indirectly references data objects D, E, and F, (which are therefore members of the dependency set for barrier object M2). During the operating period between time (T1) 930 and time (T2) 950, delete requests 934 may be received for data objects D, E, and F. Delete request 932 may be processed 936 for M2. Delete processing of data objects D, E, and F may be delayed 938 during the time interval between time (T1) 930 and time (T2) 950. Data object A, B, and C may now be available for delete processing as independent data objects with delete requests before T0. Once the dependent delete queue has processed (deleting M2), data objects from before T1 may be selected from the independent delete queue, resulting in delete processing 940 of the delete operations for data objects A and B. In some embodiments, delete processing 936 for M2 and delete processing 940 for data objects A and B may occur in any order and may use parallel processing, as long as delete processing 936 for M2 is guaranteed to complete before T2. In the example show, time and resources may be inadequate to complete processing of data object C and it remains in the independent delete queue until after T2.

At time (T2) 950 and going forward, other barrier object (Mn) delete requests 952 may be generated. While no other data objects are shown in the example, there may be other data objects in the source bucket associated with dependent data objects Mn that could be subject to future delete requests 954. Additional dependent delete requests (Mn) 952 may be generated in later time intervals and processed 956 ahead of their corresponding dependency set (x) of delete requests 954, which may be delayed 958 to a following interval. Each subsequent set of delete requests for dependency data objects may be delayed in the time interval it is received to allow the dependent data object to be delete processed. The independent delete queue may be processed up to the prior time, allowing delete processing and replication 960 of delete operations for data objects C, D, E, F, and other data objects that may follow. The process may continue in cycles of between marker generation for a number of times (Tn) 970 and barrier objects (Mn) 972.

FIG. 10 shows data object delete requests being processed in data store 1010 of a distributed storage system 1000 that includes dependent data objects, such as markers 1012.1, 1012.2, and is configured with sharded operations logs (J, K) 1030, 1040. In some embodiments, distributed storage system 1000 may be configured according to the systems and elements described with regard to FIGS. 1-5 . In the example shown, data store 1010 stores various data objects (A, B, D, and E) 1014 and dependent data objects M1 (referencing E and D) 1012.1 and M2 (referencing A and B) 1012.2. Delete requests may be received for the various data objects 1012, 1014 and processed using the respective operations logs 1030, 1040. In some embodiments, coordination of the delete processing of delete operations between operations logs 1030, 1040 may be managed using successive time points (T0, T1, T2, Tn) 1050, 1054, 1062, 1068. Each delete operation may be registered in their respective operations logs 1030, 1040 according to their local times 1032, 1042, but may only be released or queued for processing at time points (T0, T1, T2, Tn) 1050, 1054, 1062, 1068 coordinated between the shards. As previously described, time points (T0, T1, T2, Tn) 1050, 1054, 1062, 1068 may relate to post-processing the previously registered log entries and may not correspond to the local time stamps or time scales. Note that the operations shown may include only delete-related operations. Other storage operations, such as data object writes and reads, may be occurring along these time points and being logged in the respective operations logs 1030, 1040, but have been omitted for simplicity.

Time (T0) 1050 may represent initiation of the delete processing and/or the completion of a prior iteration of delete processing. Dependent delete operation 1052 for M1 may be released for processing any time after it is logged and before the next time (T1) 1054. Time (T1) 1054 may release delete operations 1056, 1058 for the dependency set data object E, D for M1. Note that time T1 may be dependent on completion of processing dependent delete operation 1052 from log (J) 1030 and still provides the release condition for dependency set delete operations 1056, 1058 in log (K) 1040. A next dependent delete operation 1060 for M2 may also be processed after T1 and may be a precondition to the next time (T2) 1062. After time (T2) 1062, the dependency set delete operations 1064, 1066 for data objects A and B may be released for processing. Again, even though dependent delete operation 1060 is logged in log (K) 1040, time (T2) 1062 may provide a global event for releasing delete operation processing for dependency set data objects A, B in both operations logs (J, K) 1030, 1040. This iterative process for processing delete operations from sharded operations logs may continue for additional dependent delete operations and time intervals after time (Tn) 1068.

As shown in FIG. 11 , the object storage system 500 may be operated according to an example method for transferring data objects using barrier objects, i.e. according to the method 1100 illustrated by the blocks 1102-1126 of FIG. 11 .

At block 1102, data objects may be stored in a data store. For example, a storage interface for the object storage system may write data objects to a data store in a distributed storage system based on one or more client applications supported by the object storage system.

At block 1104, a barrier object may be identified among the data objects. For example, a barrier engine may identify or determine one or more dependent data objects that may be treated as barrier objects to ensure that, when the dependent data objects are replicated, they are valid when received by the destination data store.

At block 1106, a dependency set of data objects may be identified among the data objects for each barrier object. For example, the barrier engine may identify or determine a set of data objects including at least one data object (and not including the barrier object itself) that is required on the destination system for the dependent data object to be valid.

At block 1108, data objects may be transferred or replicated to a second or destination data store. For example, a replication manager may identify data objects to be transferred to another object storage system or shard in a replication queue and the queued data objects may include the dependency set and the barrier object itself.

At block 1110, the barrier object may be delayed to ensure that the dependency set is transferred first. For example, the replication manager may identify the barrier object to be held in a secondary queue or similar location and monitor the transfer of the dependency set for completion.

At block 1112, transfer of the data objects, including the dependency set, may continue. For example, the dependency set may be included in an active replication queue to be allocated to replication engines for parallel replication to the destination data store along with other data objects and, in some cases, other replication requests to other data stores.

At block 1114, whether or not the dependency set has been completely transferred may be evaluated. For example, the replication manager may monitor progress through the dependency set and trigger further processing when a progress value representing completion of all transfers for the dependency set is met or exceeded. If no, the dependency set may need more time to complete and method 1100 may return to block 1112. If yes, transfer of the dependency set may be complete and method 1100 may proceed to block 1116.

At block 1116, the barrier object may be transferred to the destination object store. For example, the replication engine may return the barrier object to the primary replication queue, releasing the hold on the barrier object, and allocate it to the replication engines for replication to the destination storage system.

Block 1120-1126 may represent example methods supporting the identification of barrier objects at block 1104. At block 1120, a barrier object request may be received from a client application. For example, the storage interface may support one or more special commands that include parameters designating a received object storage request as targeting a dependent data object that should be treated as a barrier object.

At block 1122, a barrier object may be detected from the object name. For example, the storage interface may enforce a naming convention, such as a prefix or extension to the object name, that designates a data object as a barrier object.

At block 1124, a barrier object may be detected from object metadata. For example, the storage interface or barrier engine may monitor object metadata for specific fields, flags, values, or other metadata parameters that identify the corresponding data object as a barrier object.

At block 1126, a barrier object may be generated by the object storage system. For example, the barrier engine may be configured to generate barrier objects, such as snapshots, on a regular basis to support application and/or data management requirements.

As shown in FIG. 12 , the object storage system 500 may be operated according to an example method for generating barrier objects, i.e. according to the method 1200 illustrated by the blocks 1202-1220 of FIG. 12 . In some embodiments, method 1200 may operating in conjunction with one or more blocks of method 1100 in FIG. 11 .

At block 1202, data objects may be stored in a data store. For example, a storage interface for the object storage system may write data objects to a data store in a distributed storage system based on one or more client applications supported by the object storage system. Data object may be continuously stored, read, and otherwise manipulated during the operation of the object storage system and the client applications it supports.

At block 1204, an interval for barrier generation may be evaluated to determine whether the interval has elapsed. For example, a barrier engine may monitor a clock value and compare the elapsed time to a predetermined interval since the last barrier object was generated. If no, the interval has not elapsed and method 1200 may return to block 1202 to continue data storage operations. If yes, the interval has elapsed and method 1200 may proceed to block 1206.

At block 1206, a barrier object may be generated. For example, the barrier engine or storage interface may trigger a snapshot of the present state of the object store or one or more components thereof. The validity of the snapshot may be dependent on the underlying data objects referenced in the snapshot being present in the same object store as the snapshot.

At block 1208, a dependent object identifier may be set for the new barrier object. For example, the snapshot data object may have a dependent object identifier, such as a keyword, naming convention, metadata flag, or similar parameter value, added to the object data and/or object metadata to identify the snapshot as a dependent data object and barrier object.

At block 1210, the barrier object may be held to prevent replication or other operation processing. For example, a replication manager may identify the barrier object to be held in a separate queue, register, or other data structure until the dependency set of the barrier object has been successfully transferred.

At block 1212, data objects may be transferred in parallel using a plurality of replication engines. For example, the replication manager may maintain a replication queue of data objects ready for transfer to one or more other object stores and may allocate data objects for transfer to a plurality of replication engines for parallel replication.

At block 1214, whether or not the dependency set has been completely transferred may be evaluated. For example, the replication manager may monitor progress through the dependency set and trigger further processing when a progress value representing completion of all transfers for the dependency set is met or exceeded. If no, the dependency set may need more time to complete and method 1200 may return to block 1212. If yes, transfer of the dependency set may be complete and method 1200 may proceed to block 1216.

At block 1216, the barrier object may be transferred to the destination object store. For example, the replication engine may return the barrier object to the primary replication queue, releasing the hold on the barrier object, and allocate it to the replication engines for replication to the destination storage system.

At block 1218, the barrier object may be released and/or confirmed to one or more other systems or users. For example, the replication request or operation for the barrier object may have a pending or held status while the dependency set of data objects are being transferred and, upon transfer of the barrier object at block 1216, the status may change to complete and/or an appropriate confirmation or response message may be sent to relevant users or systems.

At block 1220, progress of the transfer of the dependency set may be monitored. For example, the replication manager and/or barrier engine may monitor a progress value for the replication operation against dependency set of data objects and the progress value may be used for the evaluation at block 1214.

As shown in FIG. 13 , the object storage system 500 may be operated according to an example method for managing dependent operations across data store shards, i.e. according to the method 1300 illustrated by the blocks 1302-1328 of FIG. 13 .

At block 1302, data objects may be stored in a sharded data store. For example, a storage interface for the object storage system may write data objects to a plurality of shards hosted on different storage nodes, where the shards are configured to replicate the data objects to other shards. Data objects may be continuously stored, read, and otherwise manipulated during the operation of the object storage system based on one or more client applications supported by the object storage system.

At block 1304, log entries may be generated for each shard. For example, a storage interface may generate an operations log entry in the operations log for a shard as each data operation is processed by that shard.

At block 1306, one or more dependent data objects may be identified in the plurality of shards. For example, a barrier engine may identify a barrier object that is dependent on a dependency set of data objects in the operations log in each shard.

At block 1308, dependent operation entries for each dependent data object in each shard may be identified. For example, the barrier engine may identify or determine a set of data objects including at least one data object (and not including the barrier object itself) that is required on the destination system for the dependent data object to be valid. In some embodiments, time-based operations log entries may enable the barrier engine to designate the dependency set of data objects using timestamp values, where the dependency set of data object are objects with a creation timestamp preceding an event timestamp, such as the creation timestamp of the dependent data object in that shard.

At block 1310, an operation state may be determined for each shard. For example, a shard manager may determine or identify the progress value of one or more log-based operations, such as a replication operation, proceeding through log entries in the operations log for each shard.

At block 1312, barrier indicators may be set in each shard for their respective dependent data objects. For example, the barrier engine may store barrier indicators in operations log entries for the dependent data objects in each shard to identify them as barrier objects and use their timestamps as a progress condition for releasing the barrier indicators.

At block 1314, the identified operations may continue in each shard by processing a next operations log entry. For example, the operations may continue from the operation state identified at block 1310 by selecting each sequential operations log entry in timestamp order, such as the replication manager adding the next data object identified in the operations log for that shard to the replication queue.

At block 1316, whether or not the operations log entry being processed includes a barrier indicator may be evaluated. For example, the replication manager may check the operations log entry for the barrier indicator prior to processing the data object referenced in the log entry. If no, the data object in the operations log entry is not a barrier object and method 1300 may return to block 1320. If yes, the data object is a barrier object and method 1300 may proceed to block 1318.

At block 1318, the dependent operation entry for the dependent data object may be delayed based on the barrier indicator. For example, the operation manager, such as the replication manager, may skip the dependent operation entry and place it in a secondary queue or otherwise identify it for processing once the barrier release conditions are met.

At block 1320, the operation is executed against the selected operations log entry and/or corresponding data object. For example, the replication manager may add the data object to the replication queue and then allocate it to a replication engine for transfer to the destination shard. Each operation may proceed independently within its shard based on available resources other than the delay of dependent operation entries indicated by the barrier indicators.

At block 1322, whether or not the dependency set has been completely processed may be evaluated for each shard. For example, the shard manager may check progress through the dependency set in each shard and trigger further processing when the progress timestamp value exceeds the barrier indicator, such as the barrier timestamp value, for the dependency set in each shard. If no, not all dependency sets are complete and method 1300 may return to block 1314. If yes, the release condition has been met across all shards and method 1300 may proceed to block 1324.

At block 1324, the barrier indicators for this iteration of the operations may be removed in each shard. For example, the barrier engine may remove the barrier indicators from each dependent data operation entry.

At block 1326, the hold on the dependent operation entry may be released. For example, the replication manager may return to the dependent operation entry to process the dependent data object and replicate it to the destination shard by returning to block 1314 without the barrier indicator in the entry.

At block 1328, progress of the operations in each shard may be monitored to determine a current operation state in each shard. For example, the shard manager may monitor the progress timestamp value for the operation against dependency set of data objects in each shard and the progress timestamp values may be used for the evaluation at block 1322.

As shown in FIG. 14 , the object storage system 500 may be operated according to an example method for managing dependent delete operations in a data store, i.e. according to the method 1400 illustrated by the blocks 1402-1422 of FIG. 14 .

At block 1402, data objects may be stored in a data store. For example, a storage interface for the object storage system may write data objects to an object store and some of the objects may be dependent data objects with corresponding dependency sets of other data objects. Data objects may be continuously stored, read, and otherwise manipulated during the operation of the object storage system based on one or more client applications supported by the object storage system.

At block 1404, operations log entries may be generated and stored in an operations log for the data store. For example, a storage interface may generate an operations log entry in the operations log for each data operation processed, such as data operations responsive to client data requests and/or background operations.

At block 1406, a data request may be evaluated for whether it is a delete request or not. For example, as the storage interface receives or generates data requests targeting data objects in the data store, the storage interface may determine whether a delete command or marker is associated with the request. If yes, the data request is a delete request and method 1400 may proceed to block 1408. If no, the data request is not a delete request, further delete processing is not needed, and method 1400 may return to block 1404.

At block 1408, the delete request may be evaluated for whether it targets a dependency set object. For example, a barrier engine may include data structures or logic for identifying whether a dependent data object has a dependency to the target data object. If yes, the target data object impacts the validity of a dependent data object and method 1400 may proceed to block 1412. If no, the target data object does not impact the validity of a dependent data object and method 1400 may proceed to block 1410.

At block 1410, the target data object may be delete processed without regard to any dependent data objects. For example, a delete manager may execute a delete process for the target data object using a conventional delete process.

At block 1412, a dependent delete operation may be determined. For example, one or more dependent data objects with dependency on the target data object may be identified and a corresponding delete operation for deleting the dependent data object may be determined. In some embodiments, the dependent delete operation may be generated for the dependent data object in response to the delete request for the target data object.

At block 1414, the dependency set of delete operations may be determined for the dependent delete request. For example, more than one delete request may be received for a plurality of data objects in the dependency set of the dependent data object and the delete manager may determine all data requests and related delete operations impacting the dependency set of data objects. In some instances, the target data object may be the only dependency set data object with a pending delete operation.

At block 1416, the delete process may be executed for the dependent data object. For example, the delete manager may prioritize the delete processing of the dependent delete operation for the dependent data object to assure that the dependent data object is deleted before any of the data objects in the dependency set.

At block 1418, the delete process for any delete operations targeting the dependency set of data objects may be delayed. For example, the delete manager may delay the delete processing for the dependency delete operations until deletion of the dependent data object is complete.

At block 1420, whether the deletion of the dependent data object is complete may be evaluated. For example, the delete manager may monitor for successful completion of the delete process targeting the dependent data object. If no, the dependent data object has not yet been deleted and method 1400 may return to block 1418. If yes, the dependent data object has been deleted and method 1400 may proceed to block 1422.

At block 1422, the delete process may be executed for one or more data objects in the dependency set. For example, once the dependent data object has been deleted, any delete requests and/or pending dependency set delete operations for the dependency set of data objects may be processed without concern for invalidating the dependent data object.

As shown in FIG. 15 , the object storage system 500 may be operated according to an example method for managing dependent delete operations in a data store generating dependent data objects at predetermined intervals, i.e. according to the method 1500 illustrated by the blocks 1502-1526 of FIG. 15 .

At block 1502, data objects may be stored in a data store. For example, a storage interface for the object storage system may write data objects to an object store. Data objects may be continuously stored, read, and otherwise manipulated during the operation of the object storage system based on one or more client applications supported by the object storage system.

At block 1504, a dependent data object, such as a barrier object or marker object, may be generated. For example, a barrier engine may generate a snapshot of the data objects in a data bucket or other grouping of data objects at predetermined intervals.

At block 1506, a delete request targeting a data object referenced in the dependent data object may be received. For example, a storage interface may receive one or more client delete requests for data objects that were included in the snapshot at block 1504.

At block 1508, a delete queue may be sorted based on dependent data objects and other data objects. For example, the delete queue may be sorted into a dependent data object operation queue and an independent data object operation queue, each ordered by timestamp, such as the timestamp of the delete requests.

At block 1510, the delete operations for data objects that are not dependent data objects may be determined. For example, the other delete operations may include all data objects (other than dependent data objects) in delete requests during the most recent operating interval regardless of whether they are expressly referenced in the dependent data object generated at block 1504. These delete operations may be held in the delete queue until a future processing interval, delaying their delete processing. In some embodiments, sorting the delete queue into dependent and other data objects may be completed without specific reference to which other data objects may be in the dependency set for the dependent data objects with delete operations. By sorting the types into different lists and using an interval-based delay scheme that assures that dependent delete operations are completed and replicated in the current interval and all other delete operations are delayed until a future interval, parallel processing may be used without risk of a dependency set data object being deleted ahead of its dependent data object.

At block 1512, the dependent data object delete operation may be determined. For example, the delete manager may generate a delete request for the dependent data object to be processed as a priority delete operation from the dependent delete queue.

At block 1514, the delete request for the dependent data object may be replicated to one or more other data stores. For example, responsive to executing the delete process for the dependent data object, the delete request or delete operation may be replicated to a mirroring data store or shard.

At block 1516, delete requests from prior time periods and referenced by previously deleted dependent data objects may be appended to delete queue for execution. For example, data objects that received delete requests in prior operating intervals that have not yet been executed by the delete processor may be added to the delete queue in timestamp order.

At block 1518, the delete requests for the dependency set data objects from prior operating intervals may be processed and replicated. For example, as resources allow during the remaining time interval after all dependent data object delete operations have been processed, delete requests appended at block 1516 may be processed and replicated to a mirroring data store or shard. In some embodiments, processing and replication of the delete requests in blocks 1514 and 1518, both dependent delete requests and delete requests for prior dependency sets, may be executed in any order and/or processed in parallel, as long as block 1514 is guaranteed to complete before starting a next time interval or iteration.

At block 1520, a predetermined time interval is monitored between generations of dependent data objects. For example, the barrier engine may include an interval timer that tracks the elapsed time since the prior dependent data object was generated and triggers the generation of the next dependent data object when the predetermined interval has elapsed and any other conditions for generation are met.

At block 1522, whether the predetermined time interval has elapsed and the replication of the delete operation for the dependent data object is complete may be evaluated. For example, the interval time monitored at block 1520 may trigger evaluation of one or more additional conditions for generating the next dependent data object, including whether the prior delete replication is complete.

At block 1524, a next dependent data object may be generated. In some embodiments, method 1500 may return to block 1504 and repeat the process.

At block 1526, any dependency set delete requests not executed may be forwarded to the next iteration of the process. For example, dependency set data requests from the interval that just completed and those from any prior intervals that were still not executed may be forwarded to the next delete queue and be appended to the next delete queue at block 1516 of the next iteration.

While at least one exemplary embodiment has been presented in the foregoing detailed description of the technology, it should be appreciated that a vast number of variations may exist. It should also be appreciated that an exemplary embodiment or exemplary embodiments are examples, and are not intended to limit the scope, applicability, or configuration of the technology in any way. Rather, the foregoing detailed description will provide those skilled in the art with a convenient road map for implementing an exemplary embodiment of the technology, it being understood that various modifications may be made in a function and/or arrangement of elements described in an exemplary embodiment without departing from the scope of the technology, as set forth in the appended claims and their legal equivalents.

As will be appreciated by one of ordinary skill in the art, various aspects of the present technology may be embodied as a system, method, or computer program product. Accordingly, some aspects of the present technology may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.), or a combination of hardware and software aspects that may all generally be referred to herein as a circuit, module, system, and/or network. Furthermore, various aspects of the present technology may take the form of a computer program product embodied in one or more computer-readable mediums including computer-readable program code embodied thereon.

Any combination of one or more computer-readable mediums may be utilized. A computer-readable medium may be a computer-readable signal medium or a physical computer-readable storage medium. A physical computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, crystal, polymer, electromagnetic, infrared, or semiconductor system, apparatus, or device, etc., or any suitable combination of the foregoing. Non-limiting examples of a physical computer-readable storage medium may include, but are not limited to, an electrical connection including one or more wires, a portable computer diskette, a hard disk, random access memory (RAM), read-only memory (ROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a Flash memory, an optical fiber, a compact disk read-only memory (CD-ROM), an optical processor, a magnetic processor, etc., or any suitable combination of the foregoing. In the context of this document, a computer-readable storage medium may be any tangible medium that can contain or store a program or data for use by or in connection with an instruction execution system, apparatus, and/or device.

Computer code embodied on a computer-readable medium may be transmitted using any appropriate medium, including but not limited to, wireless, wired, optical fiber cable, radio frequency (RF), etc., or any suitable combination of the foregoing. Computer code for carrying out operations for aspects of the present technology may be written in any static language, such as the C programming language or other similar programming language. The computer code may execute entirely on a user's computing device, partly on a user's computing device, as a stand-alone software package, partly on a user's computing device and partly on a remote computing device, or entirely on the remote computing device or a server. In the latter scenario, a remote computing device may be connected to a user's computing device through any type of network, or communication system, including, but not limited to, a local area network (LAN) or a wide area network (WAN), Converged Network, or the connection may be made to an external computer (e.g., through the Internet using an Internet Service Provider).

Various aspects of the present technology may be described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus, systems, and computer program products. It will be understood that each block of a flowchart illustration and/or a block diagram, and combinations of blocks in a flowchart illustration and/or block diagram, can be implemented by computer program instructions. These computer program instructions may be provided to a processing device (processor) of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which can execute via the processing device or other programmable data processing apparatus, create means for implementing the operations/acts specified in a flowchart and/or block(s) of a block diagram.

Some computer program instructions may also be stored in a computer-readable medium that can direct a computer, other programmable data processing apparatus, or other device(s) to operate in a particular manner, such that the instructions stored in a computer-readable medium to produce an article of manufacture including instructions that implement the operation/act specified in a flowchart and/or block(s) of a block diagram. Some computer program instructions may also be loaded onto a computing device, other programmable data processing apparatus, or other device(s) to cause a series of operational steps to be performed on the computing device, other programmable apparatus or other device(s) to produce a computer-implemented process such that the instructions executed by the computer or other programmable apparatus provide one or more processes for implementing the operation(s)/act(s) specified in a flowchart and/or block(s) of a block diagram.

A flowchart and/or block diagram in the above figures may illustrate an architecture, functionality, and/or operation of possible implementations of apparatus, systems, methods, and/or computer program products according to various aspects of the present technology. In this regard, a block in a flowchart or block diagram may represent a module, segment, or portion of code, which may comprise one or more executable instructions for implementing one or more specified logical functions. It should also be noted that, in some alternative aspects, some functions noted in a block may occur out of an order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or blocks may at times be executed in a reverse order, depending upon the operations involved. It will also be noted that a block of a block diagram and/or flowchart illustration or a combination of blocks in a block diagram and/or flowchart illustration, can be implemented by special purpose hardware-based systems that may perform one or more specified operations or acts, or combinations of special purpose hardware and computer instructions.

While one or more aspects of the present technology have been illustrated and discussed in detail, one of ordinary skill in the art will appreciate that modifications and/or adaptations to the various aspects may be made without departing from the scope of the present technology, as set forth in the following claims. 

What is claimed is:
 1. A system, comprising: a processor; a memory; a first operation log stored in the memory and configured to store operation entries for a first storage node; a barrier engine, stored in the memory and executable by the processor to: determine a first dependent operation entry in the first operations log indicating a first dependent data object at a first timestamp, wherein the first dependent operation entry includes a barrier object identifier; and determine at least one data operation entry in the first operations log for a first dependency set of data objects, wherein: a validity of the first dependent data object depends on the first dependency set of data objects; and the at least one data operation entry for the first dependency set of data objects does not include the barrier object identifier; and a delete manager, stored in the memory and executable by the processor to: determine, based on the barrier object identifier, a first dependent delete request for the first dependent data object; determine at least one dependency delete request for at least one data object in the first dependency set of data objects; execute, responsive to the barrier object identifier, a delete process for the first dependent delete request for the first dependent data object, wherein the delete process includes prioritizing the first dependent delete request in at least one operation queue; and delay, based on at least one time interval and until the delete process for the first dependent delete request completes, delete processing of the at least one dependency delete request responsive to the at least one data operation entry not including the barrier object identifier.
 2. The system of claim 1, wherein: the barrier engine is further executable to monitor a first time interval from a first time to a second time; the first timestamp is no earlier than the first time and prior to the second time; and the at least one time interval includes the first time interval.
 3. The system of claim 2, further comprising: a second storage node; and a replication manager, stored in the memory and executable by the processor to replicate data objects between the first storage node and the second storage node by: replicating the first dependent delete request prior to the second time; and replicating, between the first time and the second time, dependency delete requests with timestamps prior to the first time.
 4. The system of claim 3, wherein the replication manager is further executable to replicate data objects between the first storage node and second storage node by: replicating, after the second time, the at least one dependency delete request for the first dependent data object.
 5. The system of claim 2, wherein: the barrier engine is further executable to: determine a second dependent operation entry in the first operations log indicating a second dependent data object at a second timestamp; the first dependent data object is a snapshot of the first storage node at the first time; and the second dependent data object is a snapshot of the first storage node at the second time.
 6. The system of claim 5, wherein the barrier engine is further executable to: generate the first dependent data object at the first time; determine the first time interval between the first time and the second time using a predetermined snapshot interval; and generate the second dependent data object at the second time.
 7. The system of claim 6, wherein the barrier engine is further executable to: increase, until replication of the first dependent delete request to a second storage node, the first time interval; and delay, responsive to increasing the first time interval, generation of the second dependent data object.
 8. The system of claim 1, wherein the delete manager is further executable to: generate, at a first time of the first timestamp, a first operation queue of dependent delete requests, including the first dependent delete request; and generate, at the first time, a second operation queue of delete requests, excluding the first dependent delete request and including the at least one dependency delete request for the at least one data object in the first dependency set of data objects.
 9. The system of claim 1: further comprising: a second operations log stored in the memory and configured to store operation entries for a second storage node; and a replication manager stored in memory and executable by the processor to replicate data objects between the first storage node and the second storage node; wherein: the barrier engine is further executable to determine the first dependent operation entry in the second operation log indicating the first dependent data object; and the delete manager is further executable to: execute, based on the barrier object identifier, a delete process for the first dependent delete request for the first dependent data object in both the first operations log and the second operations log; and delay, until the delete process for the first dependent delete request completes in the first operation log and the second operation log, delete processing of the at least one dependency delete request.
 10. A computer-implemented method, comprising: storing a first operations log associated with a first storage node; determining a first dependent operation entry in the first operations log indicating a first dependent data object at a first timestamp, wherein the first dependent operation entry includes a barrier object identifier; determining at least one data operation entry in the first operations log for a first dependency set of data objects, wherein: a validity of the first dependent data object depends on the first dependency set of data objects; and the at least one data operation entry does not include the barrier object identifier; determining, based on the barrier object identifier, a first dependent delete request for the first dependent data object; determining at least one dependency delete request for at least one data object in the first dependency set of data objects; executing, responsive to the barrier object identifier, a delete process for the first dependent delete request for the first dependent data object, wherein the delete process includes prioritizing the first dependent delete request in at least one operation queue; and delaying, based on at least one time interval and until the delete process for the first dependent delete request completes, the delete process for the at least one dependency delete request responsive to the at least one data operation entry not including the barrier object identifier.
 11. The computer-implemented method of claim 10, further comprising: monitoring a first time interval from a first time to a second time, wherein: the first timestamp is no earlier than the first time and prior to the second time; and the at least one time interval includes the first time interval.
 12. The computer-implemented method of claim 11, further comprising: replicating data objects between the first storage node and a second storage node by: replicating, prior to the second time, the first dependent delete request; and replicating, prior to the second time, dependency delete requests with timestamps prior to the first time.
 13. The computer-implemented method of claim 12, wherein replicating data objects between the first storage node and second storage node further includes: replicating, after the second time, the at least one dependency delete request for the first dependent data object.
 14. The computer-implemented method of claim 11, further comprising: determining a second dependent operation entry in the first operations log indicating a second dependent data object at a second timestamp, wherein: the first dependent data object is a snapshot of the first storage node at the first time; and the second dependent data object is a snapshot of the first storage node at the second time.
 15. The computer-implemented method of claim 14, further comprising: generating the first dependent data object at the first time; determining the first time interval between the first time and the second time using a predetermined snapshot interval; and generating the second dependent data object at the second time.
 16. The computer-implemented method of claim 15, further comprising: increasing, until replication of the first dependent delete request to a second storage node, the first time interval; and delaying, responsive to increasing the first time interval, generation of the second dependent data object.
 17. The computer-implemented method of claim 11, further comprising: generating a first operation queue of dependent delete requests, including the first dependent delete request; and generating a second operation queue of delete requests, excluding the first dependent delete request and including the at least one dependency delete request for the at least one data object in the first dependency set of data objects.
 18. The computer-implemented method of claim 17, further comprising: executing the delete process for the first operation queue no later than the second time; and executing the delete process for the second operation queue after the second time.
 19. The computer-implemented method of claim 10, further comprising: storing a second operations log associated with a second storage node; replicating data objects between the first storage node and the second storage node; determining the first dependent operation entry in the second operations log indicating the first dependent data object; executing, based on the barrier object identifier, a delete process for the first dependent delete request for the first dependent data object in both the first operations log and the second operations log; and delaying, until the delete process for the first dependent delete request completes in both the first operations log and the second operation log, the delete process of the at least one dependency delete request in both the first operations log and the second operations log.
 20. A system, comprising: a processor; a memory; a first operations log stored in the memory and configured to store operation entries for a first storage node; means, stored in the memory for execution by the processor, for determining a first dependent operation entry in the first operations log indicating a first dependent data object at a first timestamp, wherein the first dependent operation entry includes a barrier object identifier; means, stored in the memory for execution by the processor, for determining at least one data operation entry in the first operations log for a first dependency set of data objects, wherein: a validity of the first dependent data object depends on the first dependency set of data objects; and the at least one data operation entry does not include the barrier object identifier; means, stored in the memory for execution by the processor, for determining, based on the barrier object identifier, a first dependent delete request for the first dependent data object; means, stored in the memory for execution by the processor, for determining at least one dependency delete request for at least one data object in the first dependency set of data objects; means, stored in the memory for execution by the processor, for delete processing, responsive to the barrier object identifier, the first dependent delete request for the first dependent data object, wherein the delete processing includes prioritizing the first dependent delete request in at least one operation queue; and means, stored in the memory for execution by the processor, for delaying, based on at least one time interval and until the delete processing for the first dependent delete request completes, delete processing of the at least one dependency delete request responsive to the at least one data operation entry not including the barrier object identifier. 